Forward vehicle detection using cluster-based AdaBoost

Yeul-Min Baek; Whoi-Yul Kim

doi:10.1117/1.OE.53.10.102103

15 April 2014 Forward vehicle detection using cluster-based AdaBoost

Yeul-Min Baek, Whoi-Yul Kim

Author Affiliations +

Optical Engineering, Vol. 53, Issue 10, 102103 (April 2014). https://doi.org/10.1117/1.OE.53.10.102103

Abstract

A camera-based forward vehicle detection method with range estimation for forward collision warning system (FCWS) is presented. Previous vehicle detection methods that use conventional classifiers are not robust in a real driving environment because they lack the effectiveness of classifying vehicle samples with high intraclass variation and noise. Therefore, an improved AdaBoost, named cluster-based AdaBoost (C-AdaBoost), for classifying noisy samples along with a forward vehicle detection method are presented in this manuscript. The experiments performed consist of two parts: performance evaluations of C-AdaBoost and forward vehicle detection. The proposed C-AdaBoost shows better performance than conventional classification algorithms on the synthetic as well as various real-world datasets. In particular, when the dataset has more noisy samples, C-AdaBoost outperforms conventional classification algorithms. The proposed method is also tested with an experimental vehicle on a proving ground and on public roads, ∼62 km in length. The proposed method shows a 97% average detection rate and requires only 9.7 ms per frame. The results show the reliability of the proposed method FCWS in terms of both detection rate and processing time.

Citation Download Citation

Yeul-Min Baek and Whoi-Yul Kim "Forward vehicle detection using cluster-based AdaBoost," Optical Engineering 53(10), 102103 (15 April 2014). https://doi.org/10.1117/1.OE.53.10.102103

Published: 15 April 2014

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